Dynamic Domain Adaptation for Class-Aware Cross-Subject and Cross-Session EEG Emotion Recognition

Zhunan Li · Enwei Zhu · Ming Jin · Cunhang Fan · Huiguang He · Ting Cai +1 more

169 citations0/0 files storedIEEE Journal of Biomedical and Health Informatics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Emotion and Mood Recognition EEG and Brain-Computer Interfaces Domain Adaptation and Few-Shot Learning

Abstract

It is vital to develop general models that can be shared across subjects and sessions in the real-world deployment of electroencephalogram (EEG) emotion recognition systems. Many prior studies have exploited domain adaptation algorithms to alleviate the inter-subject and inter-session discrepancies of EEG distributions. However, these methods only aligned the global domain divergence, but overlooked the local domain divergence with respect to each emotion category. This degenerates the emotion-discriminating ability of the domain invariant features. In this paper, we argue that aligning the EEG data within the same emotion categories is important for generalizable and discriminative features. Hence, we propose the dynamic domain adaptation (DDA) algorithm where the global and local divergences are disposed by minimizing the global domain discrepancy and local subdomain discrepancy, respectively. To tackle the absence of emotion labels in the target domain, we introduce a dynamic training strategy where the model focuses on optimizing the global domain discrepancy in the early training steps, and then gradually switches to the local subdomain discrepancy. The DDA algorithm is formally implemented as an unsupervised version and a semi-supervised version for different experimental settings. Based on the coarse-to-fine alignment, our model achieves the average peak accuracy of 91.08%, 92.89% on SEED, and 81.58%, 80.82% on SEED-IV in the cross-subject and cross-session scenarios, respectively.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 169 citations exist upstream.

References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2015Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks 2018EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks 2018EmotionMeter: A Multimodal Framework for Recognizing Human Emotions 2013Differential entropy feature for EEG-based emotion classification 2017DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices 2015Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers 2021MS-MDA: Multisource Marginal Distribution Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition

Cited by

2023PR-PL: A Novel Prototypical Representation Based Pairwise Learning Framework for Emotion Recognition Using EEG Signals 2024A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications 2024GDDN: Graph Domain Disentanglement Network for Generalizable EEG Emotion Recognition 2023Cross-Cultural Emotion Recognition With EEG and Eye Movement Signals Based on Multiple Stacked Broad Learning System

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.